State of Art Techniques for Wireless Sensor Network Lifetime Maximization
1
Jitender Kumar, 2 Suman
1
M.Tech, CSED, DCR University of Science & Technology, Sonepat, Haryana, India
Assistant Professor, CSED, DCR University of Science & Technology, Sonepat, Haryana, India
2
ABSTRACT:
The rapid advancement of digital electronics and wireless communications has resulted in
more rapid development of Wireless Sensor Networks (WSN) technology . In the recent years,
WSNs have seen great development in design and applications. WSNs involve deployment of
huge number of wireless sensor nodes essentially for monitoring certain area and collecting
data and sent collecting data to the base station and further processed as per requirements.
One of the important challenges faced by WSNs is to maximize the network lifetime. State of
art techniques for maximizing lifetime of network is being presented in this paper.
Keywords: Wireless sensor networks, network lifetime, energy efficiency.
INTRODUCTION:
WSNs consisting of spatially distributed autonomous devices using sensors to cooperatively
monitor physical or environmental conditions, such as temperature, sound, vibration,
pressure, motion or pollutants, at different locations[1]. Sensor nodes in large number are
densely deployed either inside the actual phenomenon or very closed to it to monitor the
environment[3]. This fast growth of WSNs has resulted in focus being given into solving the
challenges that has to face. One such challenge is to maximise the network lifetime while the
nodes remain monitored constantly. The main tasks of a sensor node are to collect data
(monitoring), perform data aggregation, and then transmit data. Among these tasks more
energy is required in transmitting data than processing data.The most recent efforts on
optimizing the wireless sensor network lifetime have been focused on routing protocol (i.e.,
transmitting data to the base and data request from the base to the sensor node). The dense
and random deployment of sensor nodes also makes it almost impractical to recharge such a
large amount of devices . Each low-cost sensor node has only limited resources such as power,
computational ability, bandwidth and memory. Once a sensor node consumes all its battery
energy, it will “die” - disappear in the network. The network may stop to work when the
remaining sensor nodes are not sufficient to complete the assigned tasks. Energy efficiency is a
central issue in satisfying sensor network functionalities and extending system lifetime.
Figure 1.1: A typical sensor network architecture [3]
A sensor network architecture shown in Figure 1.1. depicts a scenario where fire is sensed by
sensors around it. The sensor nodes report the sensed data and communicate to the sink node
via single or multi-hop communications. One or more central controllers called sink nodes
collect and further process the data generated by the sensors. The sink node may communicate
with the users via traditional wired or wireless network infrastructures. As the sink node may
not be unattended, it is usually regarded as a node in the network with infinite (i.e. sufficiently
large) resources such as battery energy and processing capability.
Literature survey:
Wireless sensor networks are usually self-organized ad hoc networks consisting of a large
number of wireless sensor nodes with small size, low battery capacity, small processing power,
limited buffer capacity and a low-power radio. Several techniques have been presented by
researchers to enhance the functionality of WSNs.
An approximation algorithm for Target Coverage problem in WSN has been presented in [1].
This paper analyzed energy model of target coverage problem, obtain three rules to reduce
network scale. In their work they try to extend network lifetime based on minimizing energy
consumption of key target and maximizing energy efficiency of sensor node. The algorithm is
highly effective and good scalable, which has a lower computational complexity.
An approximation algorithm discussed the types of coverage problem according to different
standards presented in [2]. This paper analyzed the types of the coverage problem according
to the use of the networks, the characters of monitored areas or targets, the sensing models of
sensor nodes and so on. Using the mobility of nodes, the algorithm can move redundant nodes
to uncovered area. Although there are some limits of energy and node hardware, the algorithm
is still effective in practice.
A distributed algorithmic framework to enable sensors to determine their leep-sense
cycles based on specific coverage goals discussed in[3]. The framework prioritizing each
sensor into local cover sets and then negotiating with its neighbors for satisfying mutual
constraints.
A neighbour based topology control protocol has been proposed in[4].This paper mapped
an irregular cellular learning automation to network. This approach finally forms a proper
topology which causes to lower network's energy consumption.
A distributed algorithm approach for Sensing Coverage Problem in WSNs discussed in [5].
This paper defined the maximum sensing coverage region problem for randomly distributed
WSNs and proposed the distributed algorithm to solve this problem. The main design features
is selecting a small number of delegated sensor nodes by identifying and removing redundant
nodes in high-density networks.They does not apply this algorithm to a multi-hop routing
protocol for large scale wireless sensor networks.
A force based,grid based approach discussed in[6]. This paper approved Coverage
Strategies for WSNs aimed to review the common strategies used in solving coverage problem
in WSN. They reviewed the researches done in maximizing coverage of WSN by sensors
positioning. The strategies reviewed are categorized into three groups based on the
approaches used namely; force based, grid based or computational geometry based approach.
Theory and concepts along with the examples of the algorithms proposed using these
approaches was presented. The reviewed strategies each have their own benefits or costs.
A greedy based approach discussed in [7].This paper presented the survey of coverage
problem in sensor network. This paper described the two main challenges, namely maximizing
network lifetime and network connectivity. Various problems that are relating to coverage in
WSN are also outlined. Brief summary and comparison of existing coverage schemes is also
provided.
A heuristic based approach has been discussed in[8]. This paper considers huge number
of static sensor nodes that are used for monitoring a number of target points in the region. It
has a long runtime and therefore not feasible for large scenarios.
An approximation algorithm for Target Coverage problem inks discussed in [9].This paper
studied the network lifetime issue of the target coverage problem and consider ConstantApproximation for Target Coverage Problem in Wireless Sensor Networks. This paper define
the problem in term of reduce minimum weight sensor coverage problem, which is to find the
minimum total weight of sensors to cover a given area or a given set of targets with a given set
of weighted sensors with the help of polynomial time approximation algorithm.
A distributed based approach for solving the coverage problem in sensor networks is
discussed in[10] .This problem in a 2D space is solved with an efficient polynomial-time
algorithm. This paper shows that tackling this problem in a 3D space is still feasible with in
polynomial time.
A SNR based dynamic clustering discussed in[11].This paper presented an Efficient and
Secure Routing Protocol for WSNs through SNR Based Dynamic Clustering Mechanisms and
use clustering technique for calculating the lifetime of the WSN. This paper is not discussed
the amount of overhead involved in their proposed scheme
A dynamic programming based approach has been discussed in [12].This paper defines the
maximum sensing coverage region problem for randomly distributed WSNs and proposed the
distributed algorithm to solve this problem. This paper reduced total energy consumption in
the whole system and increased significantly network lifetime.
Comparative analysis of different algorithms along with their limitations and strengths is presented
in Table 1.
Table 1.Comparative Analysis
Paper
Approach Used
A Heuristic Greedy Optimum Algorithm Heuristic Greedy
for Target Coverage in WSNs[1]
Optimum Algorithm
A Study on the Coverage Problem in
WSNs[2]
A Distributed Algorithmic Framework
for Coverage Problems in WSNs[3]
A Self-Organized Energy Efficient
Topology Control Protocol based on
Cellular Learning Automata in
Strengths
Extend network
lifetime
Limitations
Adaptability and
Stability is not
so high
A coverage algorithm Move redundant some
with node mobility
nodes to
limits of energy
uncovered or
and node
weak-covered
hardware
areas
A distributed
The several
algorithmic approach variations of the
Dependency
Graph and the
weight functions
are currently
being explored.
Self-Organized
high number of More energy
Protocols
transmission
consumption.
ranges
WSNs(SEETCLA)[4]
An Algorithm for Sensing Coverage
Problem in WSNs[5]
distributed algorithm reduced total
approach
energy
consumption in
the whole
system and
increased
Significantly
network
lifetime.
Coverage Strategies for WSNs[6]
force based, grid
maximizing
based or
coverage of
computational
WSN by sensors
geometry based
positioning
approach
Coverage Problem in WSNs: A Survey[7] greedy method
Defined
relationship
between
coverage points
and sensors
AN Energy Efficient Algorithm For
Heuristic approach
Provide quality
Connected Target Coverage Problem IN
of service,no
WSNs[8]
restriction on
Does not apply
to multi-hop
routing protocol
for
large scale
wireless sensor
networks.
costly
For
heterogeneous
network
require different
set arrangement
Long runtime
therefore not
feasible for
Constant-Approximation for Target
Coverage Problem in WSNs[9]
The Coverage Problem in Three
Dimensional WSNs[10]
Efficient and Secure Routing Protocol
for WSNs through SNR Based Dynamic
Clustering Mechanisms[11]
Energy Balance on Adaptive Routing
the network
configuration,
has polynomial
time complexity
in worst case
dynamic
Give a
programming
polynomial time
approximation
algorithm for
target coverage
problem
distributed algorithm Work for both
approach
centralized
As well as fully
distributed
manner
independently
by each sensor
SNR-based dynamic Security,
clustering
improves
the energy
efficiency
distributed
reduced total
large scenarios.
Take
exponational
time complexity
does not give
any information
about how each
point (or
subspace)
covered by
sensors
More delay in
data delivery
costly
Protocol Considering the Sensing
Coverage Problem for WSNs[12]
and light overhead
traffic approach
energy
consumption,
increased
significantly
network lifetime
Conclusion:
Advances in WSNs technology have enabled small and low-cost sensors with the capability of
sensing various types of physical and environmental conditions, data processing, and wireless
communication. Exhaustive literature survey of various techniques to maximize network lifetime of
WSN has been presented in this paper. The strengths and limitations of different algorithms have
also been presented. We are working towards designing a distributed approach for maximizing the
lifetime in a sensor network with adjustable sensing ranges.
References:
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